Busca avançada
Ano de início
Entree


Graph-based semi-supervised classification for similar wildfire dynamics

Texto completo
Autor(es):
Berton, Lilian ; de Oliveira, Pedro Leyria ; Vega-Oliveros, Didier A.
Número total de Autores: 3
Tipo de documento: Artigo Científico
Fonte: 38TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, SAC 2023; v. N/A, p. 8-pg., 2023-01-01.
Resumo

Wildfires happen in almost all biomes worldwide, and they can cause profound environmental and socio-economical impacts. Fire regimes are emergent properties of a complex system (e.g., climate-vegetation-fire interactions), which are influenced by many factors, such as climate warming, geography, vegetation, and human activity. In particular, anthropogenic land use can alter wildfires' incidence, size, season, and severity. Consequently, it is paramount to understand fire regimes to characterize similar spatiotemporal wildfire activity between natural or human ignition dynamics. This study proposes a framework combining complex networks and machine learning to classify similar wildfire regions. From global satellite data over the past two decades, we extract graph-based features to characterize spatiotemporal fire regimes from different Tropical/Temperate/Boreal forests around the globe. With the extracted high-level features, we can classify how dynamically similar the wildfire regimes are between six studied regions: Amazon basin (AMZ), Australia (AUS), northwest U.S. (NUS), northeast China (NCN), and central Africa (CAF) and Asia (CAS). Results from well-known classification algorithms indicate that the AMZ and CAF wildfire dynamics are more similar than the other regions. Although we found that wildfires in NUS are more similar to the AUS regime, NUS has around 33% of similar behavior to wildfires in the AMZ. (AU)

Processo FAPESP: 19/26283-5 - Aprendendo pistas visuais da passagem do tempo
Beneficiário:Didier Augusto Vega Oliveros
Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
Processo FAPESP: 18/01722-3 - Aprendizado semissupervisionado via redes complexas: construção de redes, seleção e propagação de rótulos e aplicações
Beneficiário:Lilian Berton
Modalidade de apoio: Auxílio à Pesquisa - Regular
Processo FAPESP: 22/16655-5 - 38º Simpósio ACM/SIGAPP de Computação Aplicada
Beneficiário:Lilian Berton
Modalidade de apoio: Auxílio à Pesquisa - Reunião - Exterior